mirror of https://github.com/vladmandic/human
72 lines
2.5 KiB
JavaScript
72 lines
2.5 KiB
JavaScript
const tf = require('@tensorflow/tfjs-node');
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const fs = require('fs');
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const process = require('process');
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const console = require('console');
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const human = require('..'); // this resolves to project root which is '@vladmandic/human'
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const logger = new console.Console({
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stdout: process.stdout,
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stderr: process.stderr,
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ignoreErrors: true,
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groupIndentation: 2,
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inspectOptions: {
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showHidden: true,
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depth: 5,
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colors: true,
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showProxy: true,
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maxArrayLength: 1024,
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maxStringLength: 10240,
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breakLength: 200,
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compact: 64,
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sorted: false,
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getters: true,
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},
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});
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const config = {
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backend: 'tensorflow',
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console: true,
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face: {
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enabled: false,
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detector: { modelPath: 'file://models/blazeface/model.json', inputSize: 128, maxFaces: 10, skipFrames: 10, minConfidence: 0.8, iouThreshold: 0.3, scoreThreshold: 0.75 },
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mesh: { enabled: true, modelPath: 'file://models/facemesh/model.json', inputSize: 192 },
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iris: { enabled: true, modelPath: 'file://models/iris/model.json', inputSize: 192 },
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age: { enabled: true, modelPath: 'file://models/ssrnet-age/imdb/model.json', inputSize: 64, skipFrames: 5 },
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gender: { enabled: true, modelPath: 'file://models/ssrnet-gender/imdb/model.json' },
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},
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body: { enabled: true, modelPath: 'file://models/posenet/model.json', inputResolution: 257, outputStride: 16, maxDetections: 5, scoreThreshold: 0.75, nmsRadius: 20 },
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hand: {
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enabled: false,
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inputSize: 256,
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skipFrames: 10,
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minConfidence: 0.8,
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iouThreshold: 0.3,
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scoreThreshold: 0.75,
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detector: { anchors: 'file://models/handdetect/anchors.json', modelPath: 'file://models/handdetect/model.json' },
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skeleton: { modelPath: 'file://models/handskeleton/model.json' },
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},
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};
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async function detect(input, output) {
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await tf.setBackend('tensorflow');
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await tf.ready();
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logger.info('TFJS Flags:', tf.env().features);
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logger.log('Loading:', input);
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const buffer = fs.readFileSync(input);
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const image = tf.node.decodeImage(buffer);
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logger.log('Processing:', image.shape);
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const result = await human.detect(image, config);
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image.dispose();
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logger.log(result);
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// Draw detected data and save processed image
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logger.log('Saving:', output);
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}
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async function main() {
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if (process.argv.length !== 4) logger.error('Parameters: <input image> <output image>');
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else if (!fs.existsSync(process.argv[2])) logger.error(`File not found: ${process.argv[2]}`);
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else detect(process.argv[2], process.argv[3]);
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}
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main();
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